Stochastic Simulation: Markov Processes Generation

Related lectures (32)

Page 3 of 4

Explores the theory of point processes and their applications to extremes, emphasizing the Laplace functional and Kallenberg's theorem.

Covers stochastic models for binary transmission in communications systems.

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.

Explores time reversal in stationary Markov chains and the concept of detailed balance conditions.

Explores point processes in spatial analysis, focusing on spatial object dissemination and pattern detection.

Explores discrete-time Markov chains, emphasizing the concept of first passage time in communication systems.

Explores the Bonus Malus system for insurance premiums and Markov chain transition probabilities.

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Covers the estimation of R in Poisson processes, focusing on marking points and convergence.

Covers the definition and properties of Markov chains, including transition matrix and examples.